U.S. patent application number 16/724830 was filed with the patent office on 2021-06-24 for system and method for estimation of rock properties from core images.
The applicant listed for this patent is Chevron U.S.A. Inc.. Invention is credited to Paul DUKE, Bo GONG, Paul HART.
Application Number | 20210190664 16/724830 |
Document ID | / |
Family ID | 1000004595883 |
Filed Date | 2021-06-24 |
United States Patent
Application |
20210190664 |
Kind Code |
A1 |
DUKE; Paul ; et al. |
June 24, 2021 |
SYSTEM AND METHOD FOR ESTIMATION OF ROCK PROPERTIES FROM CORE
IMAGES
Abstract
A method is described for training a model that refines
estimated parameter values within core images is disclosed. The
method includes receiving multiple training image pairs wherein
each training image pair includes: (i) an unrefined core image of a
rock sample to be used for estimating rock properties, and (ii) a
refined core image of the same rock sample; generating a training
dataset from the multiple training image pairs; receiving an
initial core model; generating a conditioned core model by
training, using the multiple training image pairs, the initial core
model; and storing the conditioned core model in electronic
storage. The conditioned core model may be applied to an initial
target core image data set to generate a refined target sore image
dataset. The method may be executed by a computer system.
Inventors: |
DUKE; Paul; (Houston,
CA) ; GONG; Bo; (Houston, CA) ; HART;
Paul; (Houston, CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Chevron U.S.A. Inc. |
San Ramon |
CA |
US |
|
|
Family ID: |
1000004595883 |
Appl. No.: |
16/724830 |
Filed: |
December 23, 2019 |
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06T 3/4007 20130101;
G06T 2207/20132 20130101; G01N 15/08 20130101; G01N 2015/0846
20130101; G06T 7/30 20170101; G06T 2207/20081 20130101 |
International
Class: |
G01N 15/08 20060101
G01N015/08; G06T 7/30 20060101 G06T007/30; G06T 3/40 20060101
G06T003/40 |
Claims
1. A computer-implemented method for training a model that refines
estimated parameter values within core images, the method being
implemented in a computer system, the computer system including one
or more computer processors and non-transitory electronic storage
that stores core image data sets that correspond to boreholes
drilled through subsurface volumes of interest, the method
comprising: a. receiving, at the one or more computer processors,
multiple training image pairs wherein each training image pair
includes: (i) an unrefined core image of a rock sample to be used
for estimating rock properties, and (ii) a refined core image of
the same rock sample; b. generating, at the one or more computer
processors, a training dataset from the multiple training image
pairs; c. receiving, at the one or more computer processors, an
initial core model; d. generating a conditioned core model by
training, on the one or more computer processors using the multiple
training image pairs, the initial core model; and e. storing the
conditioned core model in the non-transitory electronic
storage.
2. The computer-implemented method of claim 1 wherein the unrefined
core image is created by coarsening the refined core image.
3. The computer-implemented method of claim 1 wherein the unrefined
core image is an image that was physically imaged at low-resolution
and the refined core image is an image that was physically imaged
at high-resolution.
4. The computer-implemented method of claim 3 wherein the unrefined
core image and the refined core image are aligned manually or
algorithmically using an image registration method.
5. The computer-implemented method of claim 1 wherein the unrefined
core image and the refined core image are 2-D and the generating
the training dataset includes one or more of cropping the images
into sub-images and image interpolation.
6. The computer-implemented method of claim 1 wherein the unrefined
core image and the refined core image are 3-D core volumes
represented as a stack of 2-D image slices and the generating the
training dataset includes one of: a. representing each 2-D image
slice independently as a 2-D grayscale image slice; b. representing
slices as sequences of 2-D composite channel image slices wherein
image channels represent a prior, a current, and a subsequent slice
in the stack; or c. representing slices as 3-D multi-channel voxels
of data.
7. The method of claim 1 wherein the conditioned core model is one
of general multi-layer convolutional models, generative adversarial
networks (GANs), U-Net model variants, or related model types
capable of performing image-to-image mapping.
8. The computer-implemented method of claim 1, further comprising:
a. obtaining an initial target core image data set; b. applying the
conditioned core model to the initial target core image data set to
generate a refined target core image data set; c. generating an
image that represents the refined target core image data set using
visual effects to depict at least a portion of the parameter values
in the refined target core image data set; and d. displaying, on a
graphical user interface, or storing, in non-transitory electronic
storage, the refined target core image.
9. The computer-implemented method of claim 8, wherein sampling
density of the estimated parameter values is higher for the refined
target core image data set than the initial target core image data
set.
10. A computer-implemented method for refining estimated parameter
values within core image data sets, the method being implemented in
a computer system, the computer system including one or more
computer processors and non-transitory electronic storage that
stores core image data sets that correspond to different subsurface
volumes of interest, wherein a given core image data set
corresponding to a given subsurface volume of interest specifies
estimated parameter values of different parameters, the method
comprising: a. receiving, at the one or more computer processors,
an initial target core image data set; b. obtaining, from the
non-transitory electronic storage, a conditioned core model, the
conditioned core model having been conditioned by training an
initial core model, wherein training data includes (i) unrefined
core image data sets specifying estimated parameter values and (ii)
refined core image data sets specifying refined estimated parameter
values within the corresponding subsurface volume of interest; c.
applying the conditioned core model to the initial target core
image data set to generate a refined target core image data set; d.
generating a refined target core image that represents the refined
target core image data set using visual effects to depict at least
a portion of refined parameter values in the refined target core
image data set; and e. displaying, on a graphical user interface,
or storing, in non-transitory electronic storage, the refined
target core image.
11. A computer system, comprising: one or more processors; memory;
and one or more programs, wherein the one or more programs are
stored in the memory and configured to be executed by the one or
more processors, the one or more programs including instructions
that when executed by the one or more processors cause the system
to: a. receive, at the one or more processors, multiple training
image pairs wherein each training image pair includes: (i) an
unrefined core image of a rock sample to be used for estimating
rock properties, and (ii) a refined core image of the same rock
sample; b. generate, at the one or more processors, a training
dataset from the multiple training image pairs; c. receive, at the
one or more processors, an initial core model; d. generate a
conditioned core model by training, on the one or more processors
using the multiple training image pairs, the initial core model;
and e. store the conditioned core model in non-transitory
electronic storage.
12. A computer system, comprising: one or more processors; memory;
and one or more programs, wherein the one or more programs are
stored in the memory and configured to be executed by the one or
more processors, the one or more programs including instructions
that when executed by the one or more processors cause the system
to: a. receive, at the one or more processors, an initial target
core image data set; b. obtain, from non-transitory electronic
storage, a conditioned core model, the conditioned core model
having been conditioned by training an initial core model, wherein
training data includes (i) unrefined core image data sets
specifying estimated parameter values and (ii) refined core image
data sets specifying refined estimated parameter values within the
corresponding subsurface volume of interest; c. apply the
conditioned core model to the initial target core image data set to
generate a refined target core image data set; d. generate a
refined target core image that represents the refined target core
image data set using visual effects to depict at least a portion of
refined parameter values in the refined target core image data set;
and e. display, on a graphical user interface, or store, in the
non-transitory electronic storage, the refined target core
image.
13. A non-transitory computer readable storage medium storing one
or more programs, the one or more programs comprising instructions,
which when executed by an electronic device with one or more
processors and memory, cause the device to: a. receive, at the one
or more processors, multiple training image pairs wherein each
training image pair includes: (i) an unrefined core image of a rock
sample to be used for estimating rock properties, and (ii) a
refined core image of the same rock sample; b. generate, at the one
or more processors, a training dataset from the multiple training
image pairs; c. receive, at the one or more processors, an initial
core model; d. generate a conditioned core model by training, on
the one or more processors using the multiple training image pairs,
the initial core model; and e. store the conditioned core model in
non-transitory electronic storage.
14. A non-transitory computer readable storage medium storing one
or more programs, the one or more programs comprising instructions,
which when executed by an electronic device with one or more
processors and memory, cause the device to: a. receive, at the one
or more processors, an initial target core image data set; b.
obtain, from non-transitory electronic storage, a conditioned core
model, the conditioned core model having been conditioned by
training an initial core model, wherein training data includes (i)
unrefined core image data sets specifying estimated parameter
values and (ii) refined core image data sets specifying refined
estimated parameter values within the corresponding subsurface
volume of interest; c. apply the conditioned core model to the
initial target core image data set to generate a refined target
core image data set; d. generate a refined target core image that
represents the refined target core image data set using visual
effects to depict at least a portion of refined parameter values in
the refined target core image data set; and e. display, on a
graphical user interface, or store, in the non-transitory
electronic storage, the refined target core image.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] Not applicable.
STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT
[0002] Not applicable.
TECHNICAL FIELD
[0003] The disclosed embodiments relate generally to techniques for
estimating pore-scale rock properties from core images
representative of subsurface reservoirs and, in particular, to a
method for estimating pore-scale rock properties from core images
at resolutions below pore-scale.
BACKGROUND
[0004] Pore-scale rock properties can be estimated from
two-dimensional or three-dimensional core images through
calculation or numerical simulation. However, accuracy of the
estimation is directly limited by the resolution of the applied
imaging technique. In rocks with complex pore systems, the pixel or
voxel sizes of the core images may not be sufficient to capture the
details of pore spaces and pore connectivities, leading to
uncertainties in determining properties like pore-size
distribution, permeability and capillary pressure. Imaging at an
extremely high resolution is time-consuming, and hence is usually
done on selected small samples, the area or volume of which is
often not large enough to represent the formation
heterogeneity.
[0005] There exists a need for a method that can use relatively
low-resolution core images to determine pore-scale rock properties
with high accuracy in large rock volumes.
SUMMARY
[0006] In accordance with some embodiments, a method for training a
model that refines estimated parameter values within core images is
disclosed. The method includes receiving multiple training image
pairs wherein each training image pair includes: (i) an unrefined
core image of a rock sample to be used for estimating rock
properties, and (ii) a refined core image of the same rock sample;
generating a training dataset from the multiple training image
pairs; receiving an initial core model; generating a conditioned
core model by training, using the multiple training image pairs,
the initial core model; and storing the conditioned core model in
electronic storage. In an embodiment, the unrefined core image is
created by coarsening the refined core image. In an embodiment, the
unrefined core image is an image that was physically imaged at
low-resolution and the refined core image is an image that was
physically imaged at high-resolution. In an embodiment, the
unrefined core image and the refined core image are aligned
manually or algorithmically using an image registration method. In
an embodiment, the unrefined core image and the refined core image
are 2-D and the generating the training dataset includes one or
more of cropping the images into sub-images and image
interpolation. In an embodiment, the unrefined core image and the
refined core image are 3-D core volumes represented as a stack of
2-D image slices and the generating the training dataset includes
one of: representing each 2-D image slice independently as a 2-D
grayscale image slice; representing slices as sequences of 2-D
composite channel image slices wherein image channels represent a
prior, a current, and a subsequent slice in the stack; or
representing slices as 3-D multi-channel voxels of data. In an
embodiment, the conditioned core model is one of general
multi-layer convolutional models, generative adversarial networks
(GANs), U-Net model variants, or related model types capable of
performing image-to-image mapping.
[0007] In another embodiment, a method for refining estimated
parameter values within core image data sets is disclosed. The
method includes obtaining an initial target core image data set;
obtaining a conditioned core model, the conditioned core model
having been conditioned by training an initial core model, wherein
training data includes (i) unrefined core image data sets
specifying estimated parameter values and (ii) refined core image
data sets specifying refined estimated parameter values within the
corresponding subsurface volume of interest; applying the
conditioned core model to the initial target core image data set to
generate a refined target core image data set; generating a refined
target core image that represents the refined target core image
data set using visual effects to depict at least a portion of
refined parameter values in the refined target core image data set;
and displaying, on a graphical user interface, or storing, in
electronic storage, the refined target core image.
[0008] In another aspect of the present invention, to address the
aforementioned problems, some embodiments provide a non-transitory
computer readable storage medium storing one or more programs. The
one or more programs comprise instructions, which when executed by
a computer system with one or more processors and memory, cause the
computer system to perform any of the methods provided herein.
[0009] In yet another aspect of the present invention, to address
the aforementioned problems, some embodiments provide a computer
system. The computer system includes one or more processors,
memory, and one or more programs. The one or more programs are
stored in memory and configured to be executed by the one or more
processors. The one or more programs include an operating system
and instructions that when executed by the one or more processors
cause the computer system to perform any of the methods provided
herein.
BRIEF DESCRIPTION OF THE DRAWINGS
[0010] FIG. 1 demonstrates varying resolution and sample volumes on
cores/core images;
[0011] FIG. 2 illustrates a flowchart of a method of rock property
estimation, in accordance with some embodiments;
[0012] FIG. 3 demonstrates steps of the method of rock property
estimation, in accordance with some embodiments;
[0013] FIG. 4 illustrates a core rock sample imaged at two
different resolutions, in accordance with some embodiments;
[0014] FIG. 5 demonstrates steps of the method of rock property
estimation, in accordance with some embodiments;
[0015] FIG. 6 demonstrates steps of the method of rock property
estimation, in accordance with some embodiments;
[0016] FIG. 7 demonstrates steps of the method of rock property
estimation, in accordance with some embodiments;
[0017] FIG. 8 demonstrates an example of a step of rock property
estimation s, in accordance with some embodiments;
[0018] FIG. 9 demonstrates steps of the method of rock property
estimation, in accordance with some embodiments;
[0019] FIG. 10 demonstrates steps of the method of rock property
estimation, in accordance with some embodiments;
[0020] FIG. 11 demonstrates steps of the method of rock property
estimation, in accordance with some embodiments;
[0021] FIG. 12 demonstrates steps of the method of rock property
estimation, in accordance with some embodiments;
[0022] FIG. 13 demonstrates steps of the method of rock property
estimation, in accordance with some embodiments;
[0023] FIG. 14 demonstrates improved rock property estimation
results of the model-enhanced method of rock property estimation,
in accordance with some embodiments; and
[0024] FIG. 15 is a block diagram illustrating a rock property
estimation system, in accordance with some embodiments.
[0025] Like reference numerals refer to corresponding parts
throughout the drawings.
DETAILED DESCRIPTION OF EMBODIMENTS
[0026] Described below are methods, systems, and computer readable
storage media that provide a manner of rock property estimation
from core images. Reference will now be made in detail to various
embodiments, examples of which are illustrated in the accompanying
drawings. In the following detailed description, numerous specific
details are set forth in order to provide a thorough understanding
of the present disclosure and the embodiments described herein.
However, embodiments described herein may be practiced without
these specific details. In other instances, well-known methods,
procedures, components, and mechanical apparatus have not been
described in detail so as not to unnecessarily obscure aspects of
the embodiments.
[0027] Rock imaging techniques, like X-ray computed tomography
(CT), microtomography (.mu.CT), scanning electron microscopy (SEM),
and confocal microscopy allow earth scientists to visualize and
analyze rock samples in extremely fine detail. The ability to
characterize micrometer- and nanometer-scale features, such as pore
size, grain surface roughness, and mineral composition is critical
for understanding fluid flow behaviors in subsurface reservoirs and
impact reservoir production forecast and development decisions.
Such rock sample images are used to reconstruct 3D models, on which
numerical simulations can be conducted to study static and dynamic
properties of the imaged rock systems via digital rock physics
(DRP) technologies. Typically, the physical size of rock samples
being imaged is directly linked to the resolution of the image:
higher-resolution images are usually acquired on samples with
smaller sizes, as they are relatively more expensive and
time-consuming to acquire, store, and process with large samples.
Lower-resolution data (coarser images) are more available on larger
volumes, which may be more statistically representative for the
properties of interest, but accuracy of the resulted
interpretations may be compromised because of the loss of fine
details. FIG. 1 illustrates the resolution and dimensions possible
for various core-related samples. Image 11 is part of a 3D X-ray CT
scan acquired on a 4-inch-diameter core at the resolution of 127
.mu.m/voxel. Image 12 is part of a 3D .mu.CT scan acquired on a
1-inch-diameter core plug at 22.7 .mu.m/voxel. Image 13 is part of
a 3D .mu.CT scan acquired on an 8-mm-diameter mini core plug at 3.5
.mu.m/voxel. Image 14 is an SEM image acquired on a thin slice of
the mini core plug, within which three confocal microscopy images
could be acquired on even smaller areas (rectangular boxes) at 0.25
.mu.m/pixel. Enhancement on lower-resolution images will improve
the characterization of detailed features on larger rock volumes,
and hence increase the accuracy of the estimation of rock
properties derived from such images.
[0028] FIG. 2 illustrates a flowchart of a method 100 for rock
property estimation from core images. At operation 10, images of at
least two different resolutions are obtained for at least one
shared volume of rock samples. Embodiments described herein
disclose a method for acquiring low-resolution/high-resolution
image pairs to serve as the source for a model training set of
images. These image pairs may come from two different processes: a)
physically imaging the sample twice at two different resolutions;
or b) deriving a low-resolution version of a high-resolution image
by degrading the high-resolution image as described below. At least
one of the lower resolutions is too low for pore-scale rock
property estimation. At least one of the higher resolutions is
desired for the purpose of estimating pore-scale rock properties
such as pore size, pore throat diameter, and grain surface
roughness.
[0029] In one embodiment as presented in FIG. 3, in order to create
data for training a core image super resolution model, a core slice
or volume is imaged at both a high-resolution and a lower
resolution 32. FIG. 4 illustrates an example of this, with SEM
image 41 being the low resolution (388 nm/pixel) image on a
relatively larger area of a rock sample (1.59 mm by 1.46 mm) and
SEM image 42 being the high resolution (62 nm/pixel) image on a
smaller area (0.254 mm by 0.234 mm, illustrated by the rectangular
box) of what is covered by image 41. The part of image 41 within
the rectangular box can then be used as an input image of the
training data and image 42 can be used as a target image. While
every effort is made to maintain consistency between the captured
images, these are 2 separate imaging processes and it is possible
that there can be variances in position, scale, and rotation
between the high-resolution and the low-resolution imaging
processes. Prior to creating a model training dataset, these
variances must be minimized as much as possible in order to have
corresponding high-resolution/low-resolution image pairs for the
same sample.
[0030] Correcting this variance can be done manually or
algorithmically. An image registration method 33 has been developed
to search for the best set of correction parameters so that the
high-resolution and low-resolution images represent the same rock
area, shown as operation 11 in FIG. 2. For 2-D image slice pairs,
this can involve changing the image scale, x-y position, and
rotation of imaged rock area. For 3-D volume image stacks, we may
additionally correct for z-position (depth).
[0031] Finding the appropriate corrections for image registration
parameters may involve a deterministic, stochastic, or an
exhaustive grid search of the parameter space or any combination of
such methods for optimizing the parameters that will best align
image pairs. The image registration loss/fitness function may
entail calculating pixel level mean squared error or other image
similarity measures such that the search algorithm can calculate
the closeness of the parameter-corrected
low-resolution/high-resolution match. For 3-D volume image stacks,
the loss/fitness function can be calculated on any subset of
corresponding images extracted from the image stacks based on the
value of the z-position parameters as depicted in FIG. 6.
[0032] FIG. 5 depicts the steps involved to obtain a pair of
low-resolution and high-resolution images or volume image stacks
from a rock sample. The sample is imaged twice--at a lower
resolution and a higher resolution. For a single slice this results
in 2 images. For a volume, this results in a stack of images
representing a rock volume. These images must then be registered to
determine any necessary shift, scaling, or rotation in order align
the two different images or image stacks. Once aligned, this set of
low-resolution/high-resolution image pairs can be used as the
source for a rock core super resolution model training dataset.
[0033] FIG. 6 depicts a parameterized method for aligning 2 images
or 2 image stacks and scoring how well they are aligned given a set
of registration parameters. Parameters may allow for any number of
adjustments to images or image stacks. In this depiction, the
parameters describe how to adjust images in two different image
stacks, including: [0034] Position and spacing in z/depth of the
high-resolution stack of images within the larger, low-resolution
image stack [0035] How to crop images in both the high-resolution
and low-resolution image stacks [0036] How to rotate the images in
the low-resolution image stack Given a set of registration
parameter values, any number of sample images can be generated at
corresponding positions within each stack and scored to determine
how well the stacks are aligned. In this depiction, pixel-wise mean
squared error is calculated as the score. When searching for the
best registration parameters this scoring method may be used as the
loss or fitness function in a stochastic or gradient search
algorithm.
[0037] Once the images are aligned, image/volume pairs can be
extracted from the aligned low-resolution/high-resolution images 34
to create a training set for model training 35 of the super
resolution model. FIG. 7 depicts one embodiment of steps for
generating pairs of input training images and target training
images from registered high-resolution, low-resolution image pairs.
The low-resolution image is used as the source for model input
training data and the high-resolution image is used as the source
for model target training images. Since these images are often very
large, in this embodiment, the images are chopped into a set of
smaller sub-images. Sub-images may be randomly sampled from the
large source images or may be regularly spaced with or without
overlap. Optionally, the model training sub-images may be further
augmented by image operations such as flipping, rotating, warping,
lightening/darkening, adjusting contrast, and others in order to
larger number of training image pairs with greater variation.
[0038] In another embodiment, synthetically created training data
may be used. This may consist, for example, of high-resolution core
images that have been coarsened by 2 times, 4 times, and so on, as
demonstrated in FIG. 8. In FIG. 8, the original high-resolution
image 81 is down-sampled 32 times to the low-resolution image 84.
Examples of image coarsening techniques include but are not limited
to averaging neighboring pixels/voxels, and randomly removing a
number of pixels/voxels. Image 84 can then be used as an input
image of the training data, and image 81 can be used as a target
image of the training data. Synthetically blurred data has the
advantage that the low-resolution image is perfectly aligned with
the original high-resolution image such that no image registration
is required. Additionally, the model designer can choose any
specific blur level or a mix of blur levels as the source training
data for training a model.
[0039] FIG. 9 illustrates an example of how the synthetic training
data may be obtained. The high-resolution image 91 may be
down-sampled to create low-resolution image 92, which has fewer
pixels/voxels than image 91. The small image 92 can then be
up-sampled/resized to create blurred image 93, which matches the
number of pixels/voxels with image 91 but provides less details.
Image up-sampling can be done using algorithms such as
nearest-neighbor interpolation, bilinear interpolation and bicubic
interpolation. Then the high-resolution image 91 can be included as
target images 95 of the training data. The blurred image 93 can be
included as input images 94 of the training data. Alternatively,
image 91 can be chopped into sub-images to generate a subset of the
target images 95 of the training data. The blurred image 93 can
also be chopped into sub-images to generate a subset of the input
images 94 of the training data.
[0040] Referring again to FIG. 2, at operation 12, as is common
practice in model training workflows, data may be separated into 3
datasets: a training dataset, a validation dataset, and a test
dataset. The training dataset is directly used by the learning
algorithm to adjust model weights. Model performance on the
validation dataset is evaluated to optimize the configuration of
the model, i.e., hyperparameter tuning and to prevent overfitting.
The test dataset is used to assess how a trained model performs on
new data, i.e., how well the trained model generalizes to new data
not contained in the training or validation set. This can be done
for 2-D data and/or 3-D data.
2D Image Training Dataset Creation
[0041] Once one or more low-resolution/high-resolution image pairs
are acquired and aligned, a model training dataset can be created
where the low-resolution version will be used as the model input
image and the high-resolution image will be used as the model
target. Since the source images can be very large, a model will
generally be trained on smaller sub-images cropped from the larger
whole images. The model input-output structure can be adjusted to
match the sub-image size chosen by the model designer. The size
selected will be determined by a variety of design factors such as
memory, cpu/gpu training performance, runtime performance, etc.
Typical sub-image sizes may range from 32.times.32 pixels to
512.times.512 pixels but are not limited to those sizes. The lower
resolution image will be smaller in size than the higher resolution
image. The training data and model can be designed to increase the
image size directly, for example, by taking a 32.times.32 pixel
image as input and generating a 128.times.128 as output. This
results in a model that is specifically designed to improve image
resolution by a factor of 4. Alternatively, the low-resolution
source image may be resized via image interpolation to match the
high-resolution target image size prior to creating the model
training dataset such that the model will process an
input/low-resolution sub-image which is the same size as the
target/high-resolution sub-image. This has the advantage that a
single model can be trained to correct for multiple resolution
improvements.
3D Image Stack Training Dataset Creation
[0042] 3D core volumes can be represented as stacks of 2D image
slices captured at regular intervals. A model can be designed to
perform core super resolution on 3D image stack volumes in a
variety of ways, for example: [0043] 1) Representing each slice
independently as a 2D grayscale image slice and using 2D
convolutional units in the model; [0044] 2) Representing slices as
sequences of 2D composite color image slices where image color
channels represent the prior, current, and subsequent slices in the
stack as depicted in FIG. 10 and using 2D convolutional units in
the model; [0045] 3) Representing slices as 3D voxels of data and
using 3D convolutional units in the model. Regardless of the chosen
3D data and model representation, image slices for any position in
the stack can be generated as a weighted combination of the known
slices before and after a specific z-location as depicted in FIG.
11. For creating model training data, the low-res image stack is
used to generate a low-res image slice for each z-position
corresponding to a high-resolution image in the hi-res image stack.
Then, the low-resolution/high-resolution image data can be shaped
into input/target pairs for 2D grayscale slices, 2D color slices,
or 3D volumes to create model training data.
[0046] Referring again to FIG. 2, a super resolution model may be
trained and tested at operation 13 using the datasets created at
operation 12. A super resolution model takes a lower resolution
image and generates an enhanced, higher resolution version of the
input image. In this application, a super resolution model refers
to any computational imaging model capable of performing
image-to-image mapping. The model input and output layers will
match the size and shape of the input and target training dataset
created for the purpose of training the model to perform the
image-to-image mapping involved in doing super resolution. The
architecture of the internal model layers may employ any the
various deep learning architectures such as general multi-layer
convolutional models, generative adversarial networks (GANs), U-Net
model variants, and related model types capable of performing
image-to-image mapping.
[0047] The model training process may employ a suitable training
regimen associated with the model architecture employed including
but not limited to supervised learning, unsupervised learning,
semi-supervised learning, transfer learning, generative-adversarial
learning and may employ commonly used strategies to avoid
overfitting during training such as early stopping, batch
normalization, dropout, weight decay, and other related
methods.
[0048] The determination of whether the result is accurate enough
may be done with a user-defined threshold or by allowing the model
to identify it. If the model is not accurate enough, more image
data may be acquired or added (operation 15), data may be augmented
(operation 16), and/or the model architecture may be modified
(operation 17). Each of the optional operations 15, 16, and 17 may
be used individually or in any combination.
[0049] The conditioned core model can be used to generate refined
core images at the high resolution desired from new images at
operation 14. As new rock samples are acquired and imaged, the
trained super resolution model can be applied in order to a
generate an enhanced resolution version of a given core image slice
or image stack volume involves multiple steps as depicted in FIG.
13.
[0050] For a single image slice, a typical sequence of steps may
include: [0051] Resizing the image using interpolation to a given
target size based on the desired level of resolution improvement
[0052] Chopping the image into smaller sub-images for processing by
the model. Sub-images may be overlapping or non-overlapping. [0053]
Using the model to generate a resolution-enhanced version of each
sub-image [0054] Re-assembling the generated sub-images into a
unified, resolution-enhanced whole image For an image stack, a
z-positioned image slice or volume is created at each z-location to
match the desired resolution enhancement then 2D slice sub-images
or 3D sub-volumes are extracted for processing by the model. The
model is used to generate resolution-enhanced
sub-images/sub-volumes which are then assembled into a unified 3D
volume image stack.
[0055] FIG. 12 and FIG. 13 illustrate how the trained super
resolution model can be used to generate higher resolution images
that can be used to estimate rock properties.
[0056] The enhanced images can then be displayed and used for
improved rock property estimations in petrophysical, geological and
geomechanical analyses at operation 18 of FIG. 2. FIG. 14
illustrates an example of how rock property estimation can be
improved with enhanced images. In this example, the property of
interest is pore size distribution, which is a frequency histogram
of pore radius in a rock sample. Image 141 demonstrates pore size
distribution computed from two images at different resolutions,
where the solid line is computed on the high-resolution image, and
the dashed line on the low-resolution image. The difference between
the two lines shows that low-resolution image cannot yield accurate
pore size distribution, because its resolution is not enough to
capture fine details in pore structures. After the low-resolution
image is enhanced with a trained model, pore size distribution is
computed and plotted as the dotted line in image 142. The pore size
distribution generated from the enhanced image matches very well
with the high-resolution image.
[0057] FIG. 15 is a block diagram illustrating a rock property
estimation system 500, in accordance with some embodiments. While
certain specific features are illustrated, those skilled in the art
will appreciate from the present disclosure that various other
features have not been illustrated for the sake of brevity and so
as not to obscure more pertinent aspects of the embodiments
disclosed herein.
[0058] To that end, the rock property estimation system 500
includes one or more processing units (CPUs) 502, one or more
network interfaces 508 and/or other communications interfaces 503,
memory 506, and one or more communication buses 504 for
interconnecting these and various other components. The rock
property estimation system 500 also includes a user interface 505
(e.g., a display 505-1 and an input device 505-2). The
communication buses 504 may include circuitry (sometimes called a
chipset) that interconnects and controls communications between
system components. Memory 506 includes high-speed random access
memory, such as DRAM, SRAM, DDR RAM or other random access solid
state memory devices; and may include non-volatile memory, such as
one or more magnetic disk storage devices, optical disk storage
devices, flash memory devices, or other non-volatile solid state
storage devices. Memory 506 may optionally include one or more
storage devices remotely located from the CPUs 502. Memory 506,
including the non-volatile and volatile memory devices within
memory 506, comprises a non-transitory computer readable storage
medium and may store data, velocity models, images, and/or geologic
structure information.
[0059] In some embodiments, memory 506 or the non-transitory
computer readable storage medium of memory 506 stores the following
programs, modules and data structures, or a subset thereof
including an operating system 516, a network communication module
518, and a imaging module 520.
[0060] The operating system 516 includes procedures for handling
various basic system services and for performing hardware dependent
tasks.
[0061] The network communication module 518 facilitates
communication with other devices via the communication network
interfaces 508 (wired or wireless) and one or more communication
networks, such as the Internet, other wide area networks, local
area networks, metropolitan area networks, and so on.
[0062] In some embodiments, the rock property module 520 executes
the operations of method 100. Rock property module 520 may include
data sub-module 525, which handles the training data and core image
data. This data is supplied by data sub-module 525 to other
sub-modules.
[0063] Training sub-module 522 contains a set of instructions 522-1
and accepts metadata and parameters 522-2 that will enable it to
execute operations 10-13 of method 100. The resolution sub-module
523 contains a set of instructions 523-1 and accepts metadata and
parameters 523-2 that will enable it to contribute to operations
14-18 of method 100. Although specific operations have been
identified for the sub-modules discussed herein, this is not meant
to be limiting. Each sub-module may be configured to execute
operations identified as being a part of other sub-modules, and may
contain other instructions, metadata, and parameters that allow it
to execute other operations of use in processing data and generate
the images. For example, any of the sub-modules may optionally be
able to generate a display that would be sent to and shown on the
user interface display 505-1. In addition, any of the data or
processed data products may be transmitted via the communication
interface(s) 503 or the network interface 508 and may be stored in
memory 506.
[0064] Method 100 is, optionally, governed by instructions that are
stored in computer memory or a non-transitory computer readable
storage medium (e.g., memory 506 in FIG. 15) and are executed by
one or more processors (e.g., processors 502) of one or more
computer systems. The computer readable storage medium may include
a magnetic or optical disk storage device, solid state storage
devices such as flash memory, or other non-volatile memory device
or devices. The computer readable instructions stored on the
computer readable storage medium may include one or more of: source
code, assembly language code, object code, or another instruction
format that is interpreted by one or more processors. In various
embodiments, some operations in each method may be combined and/or
the order of some operations may be changed from the order shown in
the figures. For ease of explanation, method 100 is described as
being performed by a computer system, although in some embodiments,
various operations of method 100 are distributed across separate
computer systems.
[0065] While particular embodiments are described above, it will be
understood it is not intended to limit the invention to these
particular embodiments. On the contrary, the invention includes
alternatives, modifications and equivalents that are within the
spirit and scope of the appended claims. Numerous specific details
are set forth in order to provide a thorough understanding of the
subject matter presented herein. But it will be apparent to one of
ordinary skill in the art that the subject matter may be practiced
without these specific details. In other instances, well-known
methods, procedures, components, and circuits have not been
described in detail so as not to unnecessarily obscure aspects of
the embodiments.
[0066] The terminology used in the description of the invention
herein is for the purpose of describing particular embodiments only
and is not intended to be limiting of the invention. As used in the
description of the invention and the appended claims, the singular
forms "a," "an," and "the" are intended to include the plural forms
as well, unless the context clearly indicates otherwise. It will
also be understood that the term "and/or" as used herein refers to
and encompasses any and all possible combinations of one or more of
the associated listed items. It will be further understood that the
terms "includes," "including," "comprises," and/or "comprising,"
when used in this specification, specify the presence of stated
features, operations, elements, and/or components, but do not
preclude the presence or addition of one or more other features,
operations, elements, components, and/or groups thereof.
[0067] As used herein, the term "if" may be construed to mean
"when" or "upon" or "in response to determining" or "in accordance
with a determination" or "in response to detecting," that a stated
condition precedent is true, depending on the context. Similarly,
the phrase "if it is determined [that a stated condition precedent
is true]" or "if [a stated condition precedent is true]" or "when
[a stated condition precedent is true]" may be construed to mean
"upon determining" or "in response to determining" or "in
accordance with a determination" or "upon detecting" or "in
response to detecting" that the stated condition precedent is true,
depending on the context.
[0068] Although some of the various drawings illustrate a number of
logical stages in a particular order, stages that are not order
dependent may be reordered and other stages may be combined or
broken out. While some reordering or other groupings are
specifically mentioned, others will be obvious to those of ordinary
skill in the art and so do not present an exhaustive list of
alternatives. Moreover, it should be recognized that the stages
could be implemented in hardware, firmware, software or any
combination thereof.
[0069] The foregoing description, for purpose of explanation, has
been described with reference to specific embodiments. However, the
illustrative discussions above are not intended to be exhaustive or
to limit the invention to the precise forms disclosed. Many
modifications and variations are possible in view of the above
teachings. The embodiments were chosen and described in order to
best explain the principles of the invention and its practical
applications, to thereby enable others skilled in the art to best
utilize the invention and various embodiments with various
modifications as are suited to the particular use contemplated.
* * * * *